Today, we released DL-Learner Build 2009-05-06. DL-Learner is a tool for learning OWL class expressions from examples and background knowledge. It extends Inductive Logic Programming (ILP) to Description Logics and the Semantic Web. Some notable features in this release are:

• a new learning algorithm (CELOE) designed specifically for extending OWL ontologies
• a Protege plugin using CELOE
• a manual for getting started using DL-Learner
• performance improvements through stochastic methods
• more learning examples, unit tests, code quality improvements

DL-Learner can be used to:

• solve general supervised Machine Learning problems using ontologies as background knowledge (given as OWL files, SPARQL endpoints, etc.), e.g. it was used to predict whether chemicals can cause cancer
• help knowledge engineers by learning definitions and subclass axioms (see the Protege plugin and another one for OntoWiki is in progress)
• generating user recommendations when browsing knowledge bases

I’d like to thank all contributors, in particular active developers and everyone who sent us valuable feedback.

Today, we released a major update of the DL-Learner plugin for Protege 4, the upcoming release of the popular ontology editor. The plugin allows you to get suggestions for axioms you may want to add to your OWL ontology by analysing the instance data using the DL-Learner framework. Again, we prepared a small screencast to give you an idea of how it works. The plugin is installable via the standard Protégé plugin mechanism (Protege 4 build 110 or higher required). This version comes with a completely new suggestion algorithm and enhanced performance. More information can be found on the plugin wiki page. Thanks to Christian Kötteritzsch for his implementation work and Nick Drummond for supporting us in integrating the plugin.

Today, we released a plugin for Protege 4, the upcoming release of the popular ontology editor. The plugin allows you to get suggestions for axioms you may want to add to your OWL ontology by analysing the instance data using the DL-Learner framework. You can get an idea of how it can help you in engineering an ontology by watching our screencast. It is very easy to download and install by simply copying a file into your Protege 4 plugin directory. More information can be found on the plugin wiki page. Thanks to Christian Kötteritzsch for implementing the plugin and a Merry Christmas to all AKSW blog readers.

Today, we released DL-Learner Build 2008-10-13. DL-Learner is a tool for learning complex class descriptions from examples and background knowledge. It extends Inductive Logic Programming to Description Logics and the Semantic Web.

Downloads are available at the sourceforge.net project page. For a list of the most important changes since the previous release (Build 2008-02-18), see the ChangeLog. Some notable features are:

• addition of a new learning algorithm, which uses background knowledge more efficiently to find solutions of learning problems
• a GUI as interface to create or modify configuration files and execute algorithms
• a fast approximate instance checking algorithm decreasing the time for example coverage checks (the most expensive operation) significantly, thereby improving overall performance
• a matured fragment extraction algorithm, which allows to grab OWL-DL fragments from large knowledge bases (using SPARQL), which contain enough relevant information to conduct concept learning, while they are small enough to reason efficiently (more information)

DL-Learner can be used to:

• solve general supervised Machine Learning problems using ontologies as background knowledge (given as OWL files, SPARQL endpoints, etc.) , e.g. it was used to predict whether chemicals can cause cancer
• help knowledge engineers by learning definitions and subclass axioms (plugins for Protégé and OntoWiki in progress)
• support searching/navigating/recommendations in knowledge bases

Hereby, we announce the availability of DL-Learner Build 2008-02-18, the second DL-Learner release. DL-Learner is a tool for learning complex classes from examples and background knowledge. It extends Inductive Logic Programming to Description Logics and the Semantic Web.

Downloads are available at the sf.net project page. For a list of the most important changes since the last release (Build 2007-08-31), see the Changelog. Most notably, DL-Learner now has a flexible component based design, which allows to extend it easily with new learning algorithms, learning problems, reasoners, and supported background knowledge sources. A new type of supported knowledge sources are SPARQL endpoints, from which DL-Learner can extract knowledge fragments, which enables learning classes even on large knowledge sources like DBpedia. Furthermore, DL-Learner now supports learning from positive examples only, inclusion axiom learning, the usage of N-Triple files as background knowledge, the OWL API reasoner interface, and has a more powerful web service interface. I’d like to thank Sebastian Hellmann, Sebastian Knappe, and Tilo Hielscher for their support.